ارائه روشی با استفاده از الگوریتم ژنتیک در تشخیص موضع افراد جامعه در رسانه‌های خبری و اجتماعی
محورهای موضوعی : عمومى
مهدی سالخورده حقیقی
1
,
سیدمحمد ابراهیمی
2
1 - دانشکده مهندسی کامپیوتر و فناوری اطلاعات - دانشگاه صنعتی سجاد - مشهد – ايران
2 - دانشکده مهندسی کامپیوتر و فناوری اطلاعات - دانشگاه صنعتی سجاد - مشهد – ايران
کلید واژه: رسانه‌های اجتماعی, رسانههاي خبری, تشخیص جوامع, تشخیص موضع افراد,
چکیده مقاله :
گزارش‌های خبری ارائه شده در رسانه‌های اجتماعی و خبری با انواع اسناد و مدارک ارائه می‌شوند و شامل موضوعاتی هستند که جوامع و نظرات مختلف را در برمی‌گیرند. آگاهی از رابطه میان افراد در اسناد می‌تواند به خوانندگان کمک کند تا یک دانش اولیه در‌خصوص موضوع و هدف در اسناد مختلف به‌دست آورند. در این مقاله، روشهای تشخیص جوامع بررسی شده و تکنیکهای مختلف خوشه‌بندی افرادی که نام آن‌ها در مجموعه‌ای از اسناد خبری آورده شده است نیز مورد بررسی قرار میگیرد. این افراد در جوامعی خوشه‌بندی می‌شوند که مواضع مرتبط با یکدیگر دارند. در این مقاله یک روش تشخیص موضع افراد جامعه مبتنی بر یک شبکه دوستی به عنوان مكانيزم پايه معرفی شده و مكانيزم تشخيص جوامع بهبود يافتهاي برمبناي آن ارائه گرديده است. در روش پیشنهادی از ساختار الگوریتم ژنتیک جهت بهبود نرخ تشخیص بهره گرفته شده است. در آزمایش‌ها معیار صحت به منظور مقايسه درنظر گرفته شده است که برای رسیدن به این هدف شاخص رَند نیز استفاده گردیده است. نتایج حاصل از آزمایش‌ها که برمبنای پایگاه‌های داده‌ی واقعی اسناد انتشار یافته در رسانه خبری گوگل نیوز در رابطه با یک موضوع خاص به‌دست‌آمده‌اند، حاکی از کارآمدی و بهره‌وری مطلوب روش پیشنهادی است.
News reports in social media are presented with large volumes of different kinds of documents. The presented topics in these documents focus on different communities and person stances and opinions. Knowing the relationships among persons in the documents can help the readers to obtain a basic knowledge about the subject and the purpose of various documents. In the present paper, we introduce a method for detecting communities that includes the persons with the same stances and ideas. To do this, the persons referenced in different documents are clustered into communities that have related positions and stances. In the presented method. Community-based personalities are identified based on a friendship network as a base method. Then by using a genetic algorithm, the way that these communities are identified is improved. The criterion in the tests is rand index of detection of these communities. The experiments are designed based on real databases that published in Google News on a particular topic. The results indicate the efficiency and desirability of the proposed method
1.A. M. Kaplan و M. Haenlein, “Users of the world, unite! The challenges and opportunities of social media,” Business Horizons, جلد 53, شماره 1, pp. 59-68, 2010.
20148. [درون خطي2. 3.A. Mislove , “Online social networks: measurement, analysis, and applications to distributed information systems,” 2009.
4.M. Sachan , D. Contractor, T. Faruquie و L. Subramaniam , “Using content and interactions for disdiscovering communities in social networks,” در Paper presented at the proceedings of the 21st international conference on world wide web, 2012.
5.D. Ganley و C. Lampe, “The ties that bind: social network principles in online communities,” Decision Support Systems, جلد 47, شماره 3, pp. 266-274, 2009.
6.M. Kuramochi و G. Karypis , “Finding frequent patterns in a large sparse graph,” Data Min Knowl Discov , جلد 11, شماره 3, p. 243–271.
7.X. Yan و J. Han, “gspan: graph-based substructure pattern mining,” در Paper presented at the Proceedings of the IEEE international conference on data mining (ICDM 2002), 2002.
8.H. Cai, V. W. Zheng و K. C.-C. Chang, “A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications,” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, جلد 30, شماره 9, 2017.
9.Z.-Y. Chen و C. C. Chen, “SCIFNET: Stance community identification of topic persons using friendship network analysis,” Knowledge-Based Systems, جلد 110, p. 30–48, 2016.
10.B. Akhgar, G. B. Saathoff, H. R. Arabnia, R. Hill, A. Staniforth و P. Saskia Bayerl, تدوين كنندگان, “Mining Social Media:Architecture, Tools, and Approaches to Detect Criminal Activity,” در Application of Big Data for Application of Big Data for National Security, Elsevier, 2015, pp. 155-172.
11.G.-J. Qi , C. Aggarwal و T. Huang, “Community detection with edge content in social media networks,” در Paper presented at the 2012 IEEE 28th international conference on data engineering
, 2012. 12.S. Borgatti و M. Everett , “Graph colorings and power in experimental exchange networks,” Soc Netw, جلد 14, شماره 3, p. 287–308, 1992. # 13.S. Nijssen و J. Kok , “A quickstart in frequent structure mining can make a difference,” در Paper presented at the Proceedings of the tenthACMSIGKDD international conference on knowledge discovery and data mining, 2004.
14.C. Troussas , M. Virvou, J. Caro و K. Espinosa , “Mining relationships among user clusters in Facebook for language learning,” در Paper presented at the international conference on computer, information and telecommunication systems (CITS), 2013.
15.C. Chen, Z.-Y. Chen و C.-Y. Wu , “An unsupervised approach for person name bipolarization using principal component analysis,” IEEE Trans. Knowl. Data Eng., جلد 24, pp. 1963-1976, 2012.
16.C. Chen و C.-Y. Wu , “Bipolar person name identification of topic documents using principal component analysis,” در Proceeding of the 23rd International Conference on Computational Linguistics, 2010.
17.C. D. Manning, P. Raghavan and H. Schütze, "An Introduction to Information Retrieval," in Cambridge University Press, New York, 2009.
18.S. B. Yudhoatmojo و M. A. Samuar, “Community Detection On Citation Network Of DBLP Data Sample Set Using LinkRank Algorithm,” در 4th Information Systems International Conference 2017, ISICO 2017, Bali, Indonesia, 2017.
19.B. Viswanath, M. Bashir , M. Crovella , S. Guha, K. Gummadi, B. Krishnamurthy و A. Mislove, “Towards detecting anomalous user behavior in online social networks.,” در Proceedings of the 23rd USENIX security symposium (USENIX Security, 2014.
20.M. Girvan و M. Newman, “Community structure in social and biological networks,” Proc Natl Acad Sci, جلد 99(12), p. 7821–6, 2002.
21.J. Gao, F. Liang , W. Fan, C. Wang , Y. Sun و J. Han, “On community outliers and their efficient detection in information networks,” در Proceedings of the 16th ACM SIGKDD international conference Proceedings of the 16th ACM SIGKDD international conference, 2010.
22.X. Chen and J. Li, "Community detection in complex networks using edge-deleting with restrictions," Physica A: Statistical Mechanics and its Applications, vol. 519, pp. 181-194, 4 2019.
23.M. Rezvani, W. Liang, . C. Liu و . J. Xu Yu , “Efficient Detection of Overlapping Communities Using Asymmetric Triangle Cuts,” IEEE Transactions on Knowledge and Data Engineering, جلد 30, شماره 11, 2018.
24.Z. Liu, B. Xiang, W. Guo , Y. Chen, K. Guo و . J. Zheng , “Overlapping Community Detection Algorithm Based on Coarsening and Local Overlapping Modularity,” IEEE Access, جلد 7, 2019.
25. T. Meng , L. Cai , . T. He , L. Chen و . Z. Deng, “Local Higher-Order Community Detection Based on Fuzzy Membership Functions,” IEEE Access, جلد 7, 2019.
26.I. Messaoudi و N. Kamel, “A multi-objective bat algorithm for community detection on dynamic social networks,” Applied Intelligence, pp. 1-18, 1 2019.
27.W. Luo, N. Lu, L. Ni, W. Zhu و W. Ding, “Local community detection by the nearest nodes with greater centrality,” Information Sciences, جلد 517, pp. 377-392, 2020.
28.W. Luo, . D. Zhang , . H. Jiang , L. Ni و . Y. Hu , “Local Community Detection With the Dynamic Membership Function,” IEEE Transactions on Fuzzy Systems, جلد 26, شماره 5, 2018.
29.X. Wan, X. Zuo و F. Song, “Solving dynamic overlapping community detection problem by a multiobjective evolutionary algorithm based on decomposition,” Swarm and Evolutionary Computation, 2020
30.Y. Zhang, Y. Liu, J. Li, J. Zhu, C. Yang, W. Yang و C. Wenc, “WOCDA: A whale optimization based,” Physica A: Statistical Mechanics and its Applications, جلد 539, 2020.
31.X. Pan, . G. Xu , . B. Wang و . T. Zhang , “A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks,” IEEE Access , جلد 7, 2019.
32.Z. Harris, “Distributional structure,” Word , جلد 10, p. 146–162, 1954.
33.H. Kanayama و T. Nasukawa, “Fully automatic lexicon expansion for domain-ori- ented sentiment analysis,” در Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2006.
34.G. Keller , “Statistics for Management and Economics,” Cengage Learning, 2008
. 35.P. Turney و M. Littman , “Measuring praise and criticism: inference of semantic orientation from association,” ACM Trans. Inf. Syst., جلد 21, p. 315–346, 2003. # 36.M. Newman, “Fast algorithm for detecting community structure in networks,” Phys. Rev., جلد 74, 2004. 37.X. Xu, N. Yuruk, Z. Feng و T. Schweiger, “SCAN: a structural clustering algo- rithm for networks,” در Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.
38.B. Yang, W. Cheung و J. Liu, “Community mining from signed social networks,” IEEE Trans. Knowl. Data Eng, جلد 19, p. 1333–1348, 2007.
39.P. Anchuri و M. Magdon-Ismai, “Communities and balance in signed networks: a spectral approach,” در Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2012.
40.T. Mitchel, Machine Learning, McGrawHill, 1997.
